OpenRaiser/Intern-Atlas huggingface dataset:
Recommendation of Compatible Outfits Conditioned on Style
Venue: arXiv cs.IR 2022
Year: 2022

Recommendation in the fashion domain has seen a recent surge in research in various areas, for example, shop-the-look, context-aware outfit creation, personalizing outfit creation, etc. The majority of state of the art approaches in the domain of outfit recommendation pursue to improve compatibility among items so as to produce high quality outfits. Some recent works have realized that style is an important factor in fashion and have incorporated it in compatibility learning and outfit generation. These methods often depend on the availability of fine-grained product categories or the presence of rich item attributes (e.g., long-skirt, mini-skirt, etc.). In this work, we aim to generate outfits conditional on styles or themes as one would dress in real life, operating under the practical assumption that each item is mapped to a high level category as driven by the taxonomy of an online portal, like outdoor, formal etc and an image. We use a novel style encoder network that renders outfit styles in a smooth latent space. We present an extensive analysis of different aspects of our method and demonstrate its superiority over existing state of the art baselines through rigorous experiments.
AdaGrid: Adaptive Grid Search for Link Prediction Training Objective
Venue: arXiv cs.LG 2022
Year: 2022

One of the most important factors that contribute to the success of a machine learning model is a good training objective. Training objective crucially influences the model's performance and generalization capabilities. This paper specifically focuses on graph neural network training objective for link prediction, which has not been explored in the existing literature. Here, the training objective includes, among others, a negative sampling strategy, and various hyperparameters, such as edge message ratio which controls how training edges are used. Commonly, these hyperparameters are fine-tuned by complete grid search, which is very time-consuming and model-dependent. To mitigate these limitations, we propose Adaptive Grid Search (AdaGrid), which dynamically adjusts the edge message ratio during training. It is model agnostic and highly scalable with a fully customizable computational budget. Through extensive experiments, we show that AdaGrid can boost the performance of the models up to $1.9\%$ while being nine times more time-efficient than a complete search. Overall, AdaGrid represents an effective automated algorithm for designing machine learning training objectives.
Detecting Unassimilated Borrowings in Spanish: An Annotated Corpus and Approaches to Modeling
Venue: arXiv cs.CL 2022
Year: 2022

This work presents a new resource for borrowing identification and analyzes the performance and errors of several models on this task. We introduce a new annotated corpus of Spanish newswire rich in unassimilated lexical borrowings -- words from one language that are introduced into another without orthographic adaptation -- and use it to evaluate how several sequence labeling models (CRF, BiLSTM-CRF, and Transformer-based models) perform. The corpus contains 370,000 tokens and is larger, more borrowing-dense, OOV-rich, and topic-varied than previous corpora available for this task. Our results show that a BiLSTM-CRF model fed with subword embeddings along with either Transformer-based embeddings pretrained on codeswitched data or a combination of contextualized word embeddings outperforms results obtained by a multilingual BERT-based model.
On the Performance of Co-existence between Public eMBB and Non-public URLLC Networks
Venue: arXiv cs.NI 2022
Year: 2022

To ensure the high level of automation required in today's industrial applications, next-generation wireless networks must enable real-time control and automation of dynamic processes with the requirements of extreme low-latency and ultra-reliable communications. In this paper, we provide a performance assessment for the co-existence of a public enhanced mobile broadband (eMBB) and a local non-public factory (URLLC) network and evaluate the network conditions under which the stringent latency and reliability requirements of factory automation applications are met. The evaluations consider both an unsynchronized and a synchronized time division duplexing (TDD) deployment between the networks, as well as scenarios both with and without any macro eMBB traffic located inside the factory. The results show that an unsynchronized deployment is possible if the isolation between the networks is sufficiently high, either as a result of a separation distance, wall loss or the use of separate frequencies for the networks. A synchronized deployment will avoid the cross-link interference, but it will not resolve the problems related to the closed access and the near-far interference. If the factory contains eMBB traffic served by the overlaid macro cells, the performance of both networks will suffer due to a high level of cross-link and near-far interference. The problems related to the cross-link interference can be resolved by synchronizing the networks, while the level of the near-far interference can be reduced by allowing the eMBB users to be connected to base stations located inside the factory. Finally, if an unsynchronized deployment is desired, the factory should be deployed on an isolated frequency.
Primality Tests and Prime Certificate
Venue: arXiv cs.LO 2022
Year: 2022

This note presents a formalisation done in Coq of Lucas-Lehmer test and Pocklington certificate for prime numbers. They both are direct consequences of Fermat little theorem. Fermat little theorem is proved using elementary group theory and in particular Lagrange theorem.
Formalizing the Ring of Adèles of a Global Field
Venue: arXiv cs.LO 2022
Year: 2022

The ring of adèles of a global field and its group of units, the group of idèles, are fundamental objects in modern number theory. We discuss a formalization of their definitions in the Lean 3 theorem prover. As a prerequisite, we formalized adic valuations on Dedekind domains. We present some applications, including the statement of the main theorem of global class field theory and a proof that the ideal class group of a number field is isomorphic to an explicit quotient of its idèle class group.
Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis
Venue: arXiv cs.CV 2022
Year: 2022

The extension of convolutional neural networks (CNNs) to non-Euclidean geometries has led to multiple frameworks for studying manifolds. Many of those methods have shown design limitations resulting in poor modelling of long-range associations, as the generalisation of convolutions to irregular surfaces is non-trivial. Motivated by the success of attention-modelling in computer vision, we translate convolution-free vision transformer approaches to surface data, to introduce a domain-agnostic architecture to study any surface data projected onto a spherical manifold. Here, surface patching is achieved by representing spherical data as a sequence of triangular patches, extracted from a subdivided icosphere. A transformer model encodes the sequence of patches via successive multi-head self-attention layers while preserving the sequence resolution. We validate the performance of the proposed Surface Vision Transformer (SiT) on the task of phenotype regression from cortical surface metrics derived from the Developing Human Connectome Project (dHCP). Experiments show that the SiT generally outperforms surface CNNs, while performing comparably on registered and unregistered data. Analysis of transformer attention maps offers strong potential to characterise subtle cognitive developmental patterns.
STeP-CiM: Strain-enabled Ternary Precision Computation-in-Memory based on Non-Volatile 2D Piezoelectric Transistors
Venue: arXiv cs.ET 2022
Year: 2022

We propose 2D Piezoelectric FET (PeFET) based compute-enabled non-volatile memory for ternary deep neural networks (DNNs). PeFETs consist of a material with ferroelectric and piezoelectric properties coupled with Transition Metal Dichalcogenide channel. We utilize (a) ferroelectricity to store binary bits (0/1) in the form of polarization (-P/+P) and (b) polarization dependent piezoelectricity to read the stored state by means of strain-induced bandgap change in Transition Metal Dichalcogenide channel. The unique read mechanism of PeFETs enables us to expand the traditional association of +P (-P) with low (high) resistance states to their dual high (low) resistance depending on read voltage. Specifically, we demonstrate that +P (-P) stored in PeFETs can be dynamically configured in (a) a low (high) resistance state for positive read voltages and (b) their dual high (low) resistance states for negative read voltages, without afflicting a read disturb. Such a feature, which we name as Polarization Preserved Piezoelectric Effect Reversal with Dual Voltage Polarity (PiER), is unique to PeFETs and has not been shown in hitherto explored memories. We leverage PiER to propose a Strain-enabled Ternary Precision Computation-in-Memory (STeP-CiM) cell with capabilities of computing the scalar product of the stored weight and input, both of which are represented with signed ternary precision. Further, using multi word-line assertion of STeP-CiM cells, we achieve massively parallel computation of dot products of signed ternary inputs and weights. Our array level analysis shows 91% lower delay and improvements of 15% and 91% in energy for in-memory multiply-and-accumulate operations compared to near-memory design approaches based on SRAM and PeFET respectively. STeP-CiM exhibits upto 8.91x improvement in performance and 6.07x average improvement in energy over SRAM/PeFET based near-memory design.
Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models
Venue: arXiv cs.CL 2022
Year: 2022

Eye tracking data during reading is a useful source of information to understand the cognitive processes that take place during language comprehension processes. Different languages account for different brain triggers , however there seems to be some uniform indicators. In this paper, we describe our submission to the CMCL 2022 shared task on predicting human reading patterns for multi-lingual dataset. Our model uses text representations from transformers and some hand engineered features with a regression layer on top to predict statistical measures of mean and standard deviation for 2 main eye-tracking features. We train an end to end model to extract meaningful information from different languages and test our model on two seperate datasets. We compare different transformer models and show ablation studies affecting model performance. Our final submission ranked 4th place for SubTask-1 and 1st place for SubTask-2 for the shared task.
Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds
Venue: arXiv cs.DS 2022
Year: 2022

We study the problem of releasing the weights of all-pair shortest paths in a weighted undirected graph with differential privacy (DP). In this setting, the underlying graph is fixed and two graphs are neighbors if their edge weights differ by at most $1$ in the $\ell_1$-distance. We give an $ε$-DP algorithm with additive error $\tilde{O}(n^{2/3} / ε)$ and an $(ε, δ)$-DP algorithm with additive error $\tilde{O}(\sqrt{n} / ε)$ where $n$ denotes the number of vertices. This positively answers a question of Sealfon (PODS'16), who asked whether a $o(n)$-error algorithm exists. We also show that an additive error of $Ω(n^{1/6})$ is necessary for any sufficiently small $ε, δ> 0$. Finally, we consider a relaxed setting where a multiplicative approximation is allowed. We show that, with a multiplicative approximation factor $k$, %$2k - 1$, the additive error can be reduced to $\tilde{O}\left(n^{1/2 + O(1/k)} / ε\right)$ in the $ε$-DP case and $\tilde{O}(n^{1/3 + O(1/k)} / ε)$ in the $(ε, δ)$-DP case, respectively.
Semantics between customers and providers: The relation between product descriptions, reviews, and customer satisfaction in E-commerce
Venue: arXiv cs.SI 2022
Year: 2022

In social commerce, users dialogue with each other on the topics related to the providers' products. However, the language customers use may vary from the language vendors use on their e-commerce websites and product descriptions. This situation can lead to possible misunderstandings in the social dialogue between customers, and incidental costs in the dialogue between customers and vendors. One possible reason for this difference is that words used by customers may have different meanings compared to those used by product description writers. We present a novel approach to measure this potential lexical-semantic gap for various e-commerce domains using an information-theoretical approach based on a large corpus of user reviews and product descriptions. Additionally, we use neural word embeddings to identify words with the highest semantic drift between reviews and descriptions as a tool to construct a ground truth for the task. We found that low levels of lexical-semantic gap are related to better customer satisfaction. Besides, this work is a step towards a better understanding of the effect of the use of language in e-commerce and social commerce. Potential applications of this technology could lead to better communication between customers and improvements in customer satisfaction, services, and revenue. Furthermore, this study opens up perspectives for applications in other domains with relationships beyond client-vendor, such as citizen-government and patient-healthcare system.
An Artificial Intelligence Browser Architecture (AIBA) For Our Kind and Others: A Voice Name System Speech implementation with two warrants, Wake Neutrality and Value Preservation of Personally Identifiable Information
Venue: arXiv cs.HC 2022
Year: 2022

Conversational commerce, first pioneered by Apple's Siri, is the first of may applications based on always-on artificial intelligence systems that decide on its own when to interact with the environment, potentially collecting 24x7 longitudinal training data that is often Personally Identifiable Information (PII). A large body of scholarly papers, on the order of a million according to a simple Google Scholar search, suggests that the treatment of many health conditions, including COVID-19 and dementia, can be vastly improved by this data if the dataset is large enough as it has happened in other domains (e.g. GPT3). In contrast, current dominant systems are closed garden solutions without wake neutrality and that can't fully exploit the PII data they have because of IRB and Cohues-type constraints. We present a voice browser-and-server architecture that aims to address these two limitations by offering wake neutrality and the possibility to handle PII aiming to maximize its value. We have implemented this browser for the collection of speech samples and have successfully demonstrated it can capture over 200.000 samples of COVID-19 coughs. The architecture we propose is designed so it can grow beyond our kind into other domains such as collecting sound samples from vehicles, video images from nature, ingestible robotics, multi-modal signals (EEG, EKG,...), or even interacting with other kinds such as dogs and cats.
Perfectly Perform Machine Learning Task with Imperfect Optical Hardware Accelerator
Venue: arXiv cs.ET 2022
Year: 2022

Optical architectures have been emerging as an energy-efficient and high-throughput hardware platform to accelerate computationally intensive general matrix-matrix multiplications (GEMMs) in modern machine learning (ML) algorithms. However, the inevitable imperfection and non-uniformity in large-scale optoelectronic devices prevent the scalable deployment of optical architectures, particularly those with innovative nano-devices. Here, we report an optical ML hardware to accelerate GEMM operations based on cascaded spatial light modulators and present a calibration procedure that enables accurate calculations despite the non-uniformity and imperfection in devices and system. We further characterize the hardware calculation accuracy under different configurations of electrical-optical interfaces. Finally, we deploy the developed optical hardware and calibration procedure to perform a ML task of predicting the intersubband plasmon frequency in single-wall carbon nanotubes. The obtained prediction accuracy from the optical hardware agrees well with that obtained using a general purpose electronic graphic process unit.
DDNeRF: Depth Distribution Neural Radiance Fields
Venue: arXiv cs.CV 2022
Year: 2022

In recent years, the field of implicit neural representation has progressed significantly. Models such as neural radiance fields (NeRF), which uses relatively small neural networks, can represent high-quality scenes and achieve state-of-the-art results for novel view synthesis. Training these types of networks, however, is still computationally very expensive. We present depth distribution neural radiance field (DDNeRF), a new method that significantly increases sampling efficiency along rays during training while achieving superior results for a given sampling budget. DDNeRF achieves this by learning a more accurate representation of the density distribution along rays. More specifically, we train a coarse model to predict the internal distribution of the transparency of an input volume in addition to the volume's total density. This finer distribution then guides the sampling procedure of the fine model. This method allows us to use fewer samples during training while reducing computational resources.
Error Identification Strategies for Python Jupyter Notebooks
Venue: arXiv cs.SE 2022
Year: 2022

Computational notebooks -- such as Jupyter or Colab -- combine text and data analysis code. They have become ubiquitous in the world of data science and exploratory data analysis. Since these notebooks present a different programming paradigm than conventional IDE-driven programming, it is plausible that debugging in computational notebooks might also be different. More specifically, since creating notebooks blends domain knowledge, statistical analysis, and programming, the ways in which notebook users find and fix errors in these different forms might be different. In this paper, we present an exploratory, observational study on how Python Jupyter notebook users find and understand potential errors in notebooks. Through a conceptual replication of study design investigating the error identification strategies of R notebook users, we presented users with Python Jupyter notebooks pre-populated with common notebook errors -- errors rooted in either the statistical data analysis, the knowledge of domain concepts, or in the programming. We then analyzed the strategies our study participants used to find these errors and determined how successful each strategy was at identifying errors. Our findings indicate that while the notebook programming environment is different from the environments used for traditional programming, debugging strategies remain quite similar. It is our hope that the insights presented in this paper will help both notebook tool designers and educators make changes to improve how data scientists discover errors more easily in the notebooks they write.
Learning the Effect of Registration Hyperparameters with HyperMorph
Venue: arXiv cs.CV 2022
Year: 2022

We introduce HyperMorph, a framework that facilitates efficient hyperparameter tuning in learning-based deformable image registration. Classical registration algorithms perform an iterative pair-wise optimization to compute a deformation field that aligns two images. Recent learning-based approaches leverage large image datasets to learn a function that rapidly estimates a deformation for a given image pair. In both strategies, the accuracy of the resulting spatial correspondences is strongly influenced by the choice of certain hyperparameter values. However, an effective hyperparameter search consumes substantial time and human effort as it often involves training multiple models for different fixed hyperparameter values and may lead to suboptimal registration. We propose an amortized hyperparameter learning strategy to alleviate this burden by learning the impact of hyperparameters on deformation fields. We design a meta network, or hypernetwork, that predicts the parameters of a registration network for input hyperparameters, thereby comprising a single model that generates the optimal deformation field corresponding to given hyperparameter values. This strategy enables fast, high-resolution hyperparameter search at test-time, reducing the inefficiency of traditional approaches while increasing flexibility. We also demonstrate additional benefits of HyperMorph, including enhanced robustness to model initialization and the ability to rapidly identify optimal hyperparameter values specific to a dataset, image contrast, task, or even anatomical region, all without the need to retrain models. We make our code publicly available at http://hypermorph.voxelmorph.net.
GTP-SLAM: Game-Theoretic Priors for Simultaneous Localization and Mapping in Multi-Agent Scenarios
Venue: arXiv cs.RO 2022
Year: 2022

Robots operating in multi-player settings must simultaneously model the environment and the behavior of human or robotic agents who share that environment. This modeling is often approached using Simultaneous Localization and Mapping (SLAM); however, SLAM algorithms usually neglect multi-player interactions. In contrast, the motion planning literature often uses dynamic game theory to explicitly model noncooperative interactions of multiple agents in a known environment with perfect localization. Here, we present GTP-SLAM, a novel, iterative best response-based SLAM algorithm that accurately performs state localization and map reconstruction, while using game theoretic priors to capture the inherent non-cooperative interactions among multiple agents in an uncharted scene. By formulating the underlying SLAM problem as a potential game, we inherit a strong convergence guarantee. Empirical results indicate that, when deployed in a realistic traffic simulation, our approach performs localization and mapping more accurately than a standard bundle adjustment algorithm across a wide range of noise levels.
Wordle is NP-hard
Venue: arXiv cs.CC 2022
Year: 2022

Wordle is a single-player word-guessing game where the goal is to discover a secret word $w$ that has been chosen from a dictionary $D$. In order to discover $w$, the player can make at most $\ell$ guesses, which must also be words from $D$, all words in $D$ having the same length $k$. After each guess, the player is notified of the positions in which their guess matches the secret word, as well as letters in the guess that appear in the secret word in a different position. We study the game of Wordle from a complexity perspective, proving NP-hardness of its natural formalization: to decide given a dictionary $D$ and an integer $\ell$ if the player can guarantee to discover the secret word within $\ell$ guesses. Moreover, we prove that hardness holds even over instances where words have length $k = 5$, and that even in this case it is NP-hard to approximate the minimum number of guesses required to guarantee discovering the secret word. We also present results regarding its parameterized complexity and offer some related open problems.